--- title: How does the US spending on kids change? author: Xuxin Zhang date: '2020-09-22' slug: post categories: - R project tags: - data visualization - gganimate subtitle: '' summary: '' authors: [] lastmod: '2020-09-22T16:33:37+08:00' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] --- 
library(tidyverse)
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
theme_set(theme_light())
library(gganimate)
## Warning: package 'gganimate' was built under R version 4.0.2
kids%>%filter(variable=="pubhealth")%>%
  ggplot(aes(x = year, y = inf_adj, group = state))+
  geom_point( alpha = 0.5)+geom_path(alpha = 0.3)+
  scale_y_log10()+
  geom_text(aes(label = state, x = year, y = inf_adj),
            family = "Times",angle = 30,check_overlap = TRUE)+
  transition_reveal(year)

top_states_unemp<-kids%>%filter(variable=="unemp")%>%
  filter(year ==2012)%>%
  arrange(desc(inf_adj))%>%
  head(3)%>%pull(state)

bottom_states_unemp<-kids%>%filter(variable=="unemp")%>%
  filter(year ==2012)%>%
  arrange(inf_adj)%>%
  head(3)%>%pull(state)


kids%>%filter(variable=="unemp")%>%
  mutate(top_bottom = case_when(state%in%top_states_unemp~"Top",
                                state%in%bottom_states_unemp~"Bottom",
                                TRUE~"Others"),
         state_plot =case_when(state%in%top_states_unemp~state,
                                    state%in%bottom_states_unemp~state,
                                    TRUE~""))%>%
  ggplot(aes(x = year, y = inf_adj,group = state))+
  geom_point(aes(size = top_bottom,color = top_bottom), alpha = 0.5,show.legend = FALSE)+
  geom_path(aes(size = top_bottom,color = top_bottom),show.legend = FALSE)+
  geom_text(aes(x = year, y = inf_adj,label = state_plot), 
            family = "Times",
            check_overlap = FALSE)+
  scale_size_manual(values = c(1.5,0.2,1.5))+
  scale_color_manual(values = c("pink","grey","lightgreen"))+
  scale_y_log10()+
  labs(x = "Year",
       y = "Inflation-adjusted spending on unemployment benefits",
       title = "How the Unemployment benefits (in $1,000s) changes")+
  theme(legend.position = "null")+
  theme_classic()+
  transition_reveal(year)

library(plotly)
## Warning: package 'plotly' was built under R version 4.0.2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
state_year_loc <- kids%>%filter(variable=="PK12ed")%>%
  mutate(year2 = as.factor(year))%>%
  mutate(state = str_to_lower(state))%>%
  group_by(state, year2)%>%summarise(inf_adj= mean(inf_adj))%>%
  right_join(map_data("state"),by =c("state"="region"))
## `summarise()` regrouping output by 'state' (override with `.groups` argument)
ggplotly(state_year_loc%>%mutate(year = as.numeric(year2)) %>%
  ggplot(aes(x = long, y = lat, group = group))+
  geom_polygon(aes(fill = inf_adj))+
  scale_fill_gradient(low = "yellow", high = "orangered")+
  facet_wrap(~year2)+
  theme_void())
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
library(ggrepel)
k12_data <- kids%>%filter(variable=="PK12ed")%>%
  filter(year%in%c(1997,2016))%>%mutate(year = as.character(year))%>%
  select(-raw,-inf_adj_perchild)%>%
  pivot_wider(names_from = year, values_from = inf_adj)

names(k12_data)<-c("state","variable","data1997","data2016")
  
ggplotly(k12_data%>%mutate(pct_change = data2016/data1997)%>%
  ggplot(aes(x = data1997, y = pct_change, group = state))+geom_point()+
  scale_x_log10()+expand_limits(x = 500000, y = 0))
k12_data%>%mutate(change = data2016-data1997)%>%
  select(-variable)%>%
  pivot_longer(data1997:data2016,names_to ="year", values_to = "adj_inf")%>%
  ggplot(aes(x = adj_inf, y = reorder(state,change)))+
  geom_point(aes(color = year),size = 3)+geom_line(size = 2,alpha = 0.1)+theme_classic()+
  labs(x = "Government spending adjusted by inflation",
       y = "State ordered by change in spending",
       title = "How the government spending on K12 education changes from 1997 to 2016")+
  scale_color_brewer(palette = "Paired")+
  scale_x_continuous(labels = scales::comma)